Comparative Evaluation of Anti-HER2 Affibody Molecules Labeled with Cu-64 Using NOTA and NODAGA

Imaging using affi body molecules enables discrimination between breast cancer metastases with high and low expression of HER2, making appropriate therapy selection possible. This study aimed to evaluate if the longer half-life of Cu-64 (T-1/2 = 12.7h) would make Cu-64 a superior nuclide compared to Ga-68 for PET imaging of HER2 expression using affibody molecules. The synthetic ZHER2: S1 affibody molecule was conjugated with the chelators NOTA or NODAGA and labeled with Cu-64. The tumor-targeting properties of Cu-64-NOTA-ZHER2: S1 and Cu-64-NODAGA-ZHER2: S1 were evaluated and compared with the targeting properties of Ga-68-NODAGA-ZHER2: S1 in mice. Both 64 Cu-NOTA-ZHER2: S1 and Cu-64-NODAGA-ZHER2: S1 demonstrated specific targeting of HER2-expressing xenografts. At 2 h after injection of Cu-64-NOTA-ZHER2: S1, Cu-64-NODAGA-ZHER2: S1, and Ga-68-NODAGAZHER2: S1, tumor uptakes did not differ significantly. Renal uptake of Cu-64-labeled conjugateswas dramatically reduced at 6 and 24 h after injection. Notably, radioactivity uptake concomitantly increased in blood, lung, liver, spleen, and intestines, which resulted in decreased tumor-to-organ ratios compared to 2 h postinjection. Organ uptake was lower for Cu-64-NODAGA-ZHER2: S1. The most probable explanation for this biodistribution pattern was the release and redistribution of renal radiometabolites. In conclusion, monoamide derivatives of NOTA and NODAGA may be suboptimal chelators for radiocopper labeling of anti-HER2 affibody molecules and, possibly, other scaffold proteins with high renal uptake

Molecular design of radiocopper-labelled Affibody molecules

The use of long-lived positron emitters Cu-64 or Cu-61 for labelling of Affibody molecules may improve breast cancer patients' stratification for HER-targeted therapy. Previous animal studies have shown that the use of triaza chelators for Cu-64 labelling of synthetic Affibody molecules is suboptimal. In this study, we tested a hypothesis that the use of cross-bridged chelator, CB-TE2A, in combination with Gly-Glu-Glu-Glu spacer for labelling of Affibody molecules with radiocopper would improve imaging contrast. CB-TE2A was coupled to the N-terminus of synthetic Affibody molecules extended either with a glycine (designation CB-TE2A-G-ZHER2:342) or Gly-Glu-Glu-Glu spacer (CB-TE2A-GEEE-ZHER2:342). Biodistribution and targeting properties of Cu-64-CB-TE2A-G-ZHER2:342 and Cu-64-CB-TE2A-GEEE-ZHER2:342 were compared in tumor-bearing mice with the properties of Cu-64-NODAGA-ZHER2:S1, which had the best targeting properties in the previous study. Cu-64-CB-TE2A-GEEE-ZHER2:342 provided appreciably lower uptake in normal tissues and higher tumor-to-organ ratios than Cu-64-CB-TE2A-GZHER2:342 and Cu-64-NODAGA-ZHER2:S1. The most pronounced was a several-fold difference in the hepatic uptake. At the optimal time point, 6 h after injection, the tumor uptake of Cu-64-CB-TE2A-GEEE-ZHER2: 342 was 16 +/- 6% ID/g and tumor-to-blood ratio was 181 +/- 52. In conclusion, a combination of the cross-bridged CB-TE2A chelator and Gly-Glu-Glu-Glu spacer is preferable for radiocopper labelling of Affibody molecules and, possibly, other scaffold proteins having high renal re-absorption

Comparative Evaluation of Anti-HER2 Affibody Molecules Labeled with Cu-64 Using NOTA and NODAGA

Imaging using affi body molecules enables discrimination between breast cancer metastases with high and low expression of HER2, making appropriate therapy selection possible. This study aimed to evaluate if the longer half-life of Cu-64 (T-1/2 = 12.7h) would make Cu-64 a superior nuclide compared to Ga-68 for PET imaging of HER2 expression using affibody molecules. The synthetic ZHER2: S1 affibody molecule was conjugated with the chelators NOTA or NODAGA and labeled with Cu-64. The tumor-targeting properties of Cu-64-NOTA-ZHER2: S1 and Cu-64-NODAGA-ZHER2: S1 were evaluated and compared with the targeting properties of Ga-68-NODAGA-ZHER2: S1 in mice. Both 64 Cu-NOTA-ZHER2: S1 and Cu-64-NODAGA-ZHER2: S1 demonstrated specific targeting of HER2-expressing xenografts. At 2 h after injection of Cu-64-NOTA-ZHER2: S1, Cu-64-NODAGA-ZHER2: S1, and Ga-68-NODAGAZHER2: S1, tumor uptakes did not differ significantly. Renal uptake of Cu-64-labeled conjugateswas dramatically reduced at 6 and 24 h after injection. Notably, radioactivity uptake concomitantly increased in blood, lung, liver, spleen, and intestines, which resulted in decreased tumor-to-organ ratios compared to 2 h postinjection. Organ uptake was lower for Cu-64-NODAGA-ZHER2: S1. The most probable explanation for this biodistribution pattern was the release and redistribution of renal radiometabolites. In conclusion, monoamide derivatives of NOTA and NODAGA may be suboptimal chelators for radiocopper labeling of anti-HER2 affibody molecules and, possibly, other scaffold proteins with high renal uptake

Information Market-Based Decision Fusion

Improved classification performance has practical real-world benefits ranging from improved effectiveness in detecting diseases to increased efficiency in identifying firms that are committing financial fraud. Multiclassifier combination (MCC) aims to improve classification performance by combining the decisions of multiple individual classifiers. In this paper, we present information market-based fusion (IMF), a novel multiclassifier combiner method for decision fusion that is based on information markets. In IMF, the individual classifiers are implemented as participants in an information market where they place bets on different object classes. The reciprocals of the market odds that minimize the difference between the total betting amount and the potential payouts for different classes represent the MCC probability estimates of each class being the true object class. By using a market-based approach, IMF can adjust to changes in base-classifier performance without requiring offline training data or a static ensemble composition. Experimental results show that when the true classes of objects are only revealed for objects classified as positive, for low positive ratios, IMF outperforms three benchmarks combiner methods, majority, average, and weighted average; for high positive ratios, IMF outperforms majority and performs on par with average and weighted average. When the true classes of all objects are revealed, IMF outperforms weighted average and majority and marginally outperforms average.multiclassifier combination, decision fusion, information markets, software agents